Anonymization: The imperfect science of using data while preserving privacy
Information about us, our actions, and our preferences is created at scale through surveys or
scientific studies or as a result of our interaction with digital devices such as smartphones …
scientific studies or as a result of our interaction with digital devices such as smartphones …
Reconstructing training data with informed adversaries
Given access to a machine learning model, can an adversary reconstruct the model's
training data? This work studies this question from the lens of a powerful informed adversary …
training data? This work studies this question from the lens of a powerful informed adversary …
Sok: differential privacies
D Desfontaines, B Pejó - arXiv preprint arXiv:1906.01337, 2019 - arxiv.org
Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …
Real-world trajectory sharing with local differential privacy
T Cunningham, G Cormode… - arXiv preprint arXiv …, 2021 - arxiv.org
Sharing trajectories is beneficial for many real-world applications, such as managing
disease spread through contact tracing and tailoring public services to a population's travel …
disease spread through contact tracing and tailoring public services to a population's travel …
Total variation distance privacy: Accurately measuring inference attacks and improving utility
Differential privacy (DP) is a general approach to defend against inference attacks, but hard
to balance the privacy-utility trade-off for some complex data analysis tasks. To improve the …
to balance the privacy-utility trade-off for some complex data analysis tasks. To improve the …
Learning numeric optimal differentially private truncated additive mechanisms
DM Sommer, L Abfalterer, S Zingg… - arXiv preprint arXiv …, 2021 - arxiv.org
Differentially private (DP) mechanisms face the challenge of providing accurate results while
protecting their inputs: the privacy-utility trade-off. A simple but powerful technique for DP …
protecting their inputs: the privacy-utility trade-off. A simple but powerful technique for DP …
On the Choice of Databases in Differential Privacy Composition
Differential privacy (DP) is a widely applied paradigm for releasing data while maintaining
user privacy. Its success is to a large part due to its composition property that guarantees …
user privacy. Its success is to a large part due to its composition property that guarantees …
Lowering the cost of anonymization
D Desfontaines - 2020 - research-collection.ethz.ch
The objective of this thesis is to make it easier to understand, use, and deploy strong
anonymization practices. We make progress towards this goal in three ways. First, we make …
anonymization practices. We make progress towards this goal in three ways. First, we make …
Fighting Uphill Battles: Improvements in Personal Data Privacy
DM Sommer - 2021 - research-collection.ethz.ch
With the rise of modern information technology and the Internet, the worldwide
interconnectivity is resulting in a massive collection and evaluation of potentially sensitive …
interconnectivity is resulting in a massive collection and evaluation of potentially sensitive …
Background Knowledge (B)
B Pejó, D Desfontaines - Guide to Differential Privacy Modifications: A …, 2022 - Springer
Background Knowledge (B) | SpringerLink Skip to main content Advertisement SpringerLink
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